Complex disturbances with multifrequency components inevitably exist in the attitude control system of flexible spacecrafts. Multirate iterative learning disturbance observer (MILDO) is a promising solution to estimate and attenuate these disturbances much more accurately. However, measurement delay in the attitude measurement system may severely degrade the estimation performance of MILDO and even lead to instability of the control system. To suppress complex disturbances of flexible spacecrafts subject to a known measurement delay, a delay compensation-MILDO (DC-MILDO) is proposed in this study. First, an augmented model is constructed for the delayed disturbance by introducing slowly varying disturbance, multiple periodic disturbances, and the integral of the lumped disturbance as states. Then, a virtual measurement of the system is built by realigning the control torques with the delayed measurements in spacecraft attitude dynamics. According to the augmented model and the virtual measurement, a modified MILDO is designed by using multiple iterative learning structures. Based on the estimates of the modified MILDO, the lumped disturbance at current instant can be predicted by compensating time delay in the multifrequency components. Subsequently, a composite controller for flexible spacecraft is built by combining DC-MILDO with a robust controller based on Lyapunov–Krasovskii approach. Simulation results demonstrate the effectiveness of the proposed controller.

挠性航天器的姿态控制系统不可避免地存在着具有多频分量的复杂扰动。多速率迭代学习干扰观测器（MILDO）是一种有望更准确地估计和消除这些干扰的解决方案。但是，姿态测量系统中的测量延迟可能会严重降低MILDO的估算性能，甚至导致控制系统不稳定。为了抑制挠性航天器在已知测量延迟下的复杂扰动，本研究提出了一种延迟补偿-MILDO（DC-MILDO）。首先，通过引入缓慢变化的扰动，多个周期性扰动以及集总扰动的积分作为状态，为延迟扰动构建了增强模型。然后，通过将控制扭矩与航天器姿态动力学中的延迟测量值重新对齐，可以建立系统的虚拟测量值。根据增强模型和虚拟度量，通过使用多个迭代学习结构来设计改进的MILDO。基于修改后的MILDO的估计，可以通过补偿多频分量中的时间延迟来预测当前瞬间的集总干扰。随后，通过将DC-MILDO与基于Lyapunov–Krasovskii方法的鲁棒控制器相结合，构建了用于挠性航天器的复合控制器。仿真结果证明了所提出控制器的有效性。通过使用多个迭代学习结构来设计改进的MILDO。基于修改后的MILDO的估计，可以通过补偿多频分量中的时间延迟来预测当前瞬间的集总干扰。随后，通过将DC-MILDO与基于Lyapunov–Krasovskii方法的鲁棒控制器相结合，构建了用于挠性航天器的复合控制器。仿真结果证明了所提出控制器的有效性。通过使用多个迭代学习结构来设计改进的MILDO。基于修改后的MILDO的估计，可以通过补偿多频分量中的时间延迟来预测当前瞬间的集总干扰。随后，通过将DC-MILDO与基于Lyapunov–Krasovskii方法的鲁棒控制器相结合，构建了用于挠性航天器的复合控制器。仿真结果证明了所提出控制器的有效性。